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kind: Service
apiVersion: v1
metadata:
  name: basic-ms-service
spec:
  type: NodePort
  selector:
    app: basic-ms
  ports:
  - name: basic-ms-service

raghumb

apiVersion: apps/v1
kind: Deployment
metadata:
  name: basic-ms-deployment
spec:
  replicas: 1
  selector:
    matchLabels:
      app: basic-ms
  template:

raghumb

import numpy as np 
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt 
%matplotlib inline

# end date
end_date = dt.datetime(2018, 1, 1)
# start date
start_date = end_date -  timedelta(days = 19)

raghumb

import numpy as np 
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt 
%matplotlib inline

weight_kg = np.random.rand(30)

weight_pound = weight_kg * 2.2
lines = plt.plot(weight_kg,weight_pound)

raghumb

import numpy as np 
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt 
%matplotlib inline

# Dataframe with random integers 1-100 for Age
age = pd.DataFrame(np.random.randint(1,60,size=(20, 1)), columns=list('A'))
# Dataframe with log values (salary)
salary = np.log10(age['A'])

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# All columns will be filled with NaN, replace NaN with zero
df = df.fillna(0)
print df

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# Create dataframe with date as index
# end date
end_date = dt.datetime(2018, 1, 1)
# start date is 
start_date = end_date -  timedelta(days = 5)
dates = pd.date_range(start_date, end_date)
columns = ['Defects','tasks']

# Create Dataframe
df = pd.DataFrame(index=dates, columns=columns)

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arr = np.array([[5, 2, 3, 2], [5, 1, 3, 6],[5, 1, 3, 2]])
# Create Dataframe from Numpy Array
df1 = pd.DataFrame(arr)
# Retrieve first two rows and last two columns
df2 = df1.iloc[0:2,1:3]
print df2

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print 'loop using ix - deprecated'
for i in range(df1.size):
    val = df1.ix[i]
    print val

print 'loop using iloc'
for i in range(df1.size):
    val = df1.iloc[i]

raghumb

import numpy as np
import pandas as pd
import datetime as dt
from datetime import timedelta


a = np.array([100,100,1000,0,0])
# Create Dataframe from Numpy Array
df1 = pd.DataFrame(a)